Ranking neural checkpoints is crucial. Some domains or applications lack large-scale human-curated data, like medical images [1]}, raising a pressing need for high-quality pre-trained checkpoints as a warm start for fine-tuning. Fortunately, there exist hundreds of thousands of checkpoints of popular neural network architectures. For instance, many computer vision models are built upon ResNet [2]}, Inception-ResNet [3]}, and VGG [4]}. As a result, we can construct a candidate pool by collecting the checkpoints released by different groups, for various tasks, and over distinct datasets.
